The Thirty-Seventh Annual
T.G. Ostrom Lecture
Dr. Veerabhadran Baladandayuthapani
"The Art (and Science) of Biomedical Data Integration"
Wednesday, April 11, 2018
7:00pm - Wegner G50
Please join us for an informative discussion by this year's invited guest lecturer Dr. Veerabhadran Baladandayuthapani, Department of Biostatistics, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Modern biomedicine has generated unprecedented amounts of data. A combination of clinical, environmental and public health information, proliferation of associated genomic information, and increasingly complex digital information have created unique challenges in assimilating, organizing, analyzing and interpreting such structured as well as unstructured data. Each of these distinct data types provides a different, partly independent and complementary, high-resolution view of various biological processes. Modeling and inference in such studies is challenging, not only due to high dimensionality, but also due to presence of structured dependencies (e.g. pathway/regulatory mechanisms, serial and spatial correlations etc.). Integrative analyses of these multi-domain data combined with patients’ clinical outcomes can help us understand the complex biological processes that characterize a disease, as well as how these processes relate to the eventual progression and development of a disease. This talk will cover statistical and computational frameworks that acknowledge and exploit these inherent complex structural relationships for both biomarker discovery and clinical prediction to aid translational medicine. The approaches will be illustrated using several case examples in oncology.
A reception with refreshments will immediately follow in the Hacker Lounge in Neill Hall, room 216.About Dr. Baladandayuthapani:
Dr. Baladanayuthapani is a statistician and (broadly) a data scientist, working at the intersection of statistics, biology and medicine. His research interests are mainly in high-dimensional data modeling and Bayesian inference. This includes functional data analyses, Bayesian graphical models, Bayesian semi-/non parametric models and machine learning. These methods are motivated by large and complex datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. A special focus is on developing integrative models combining different sources of data for biomarker discovery and clinical prediction to aid precision/translational medicine. His research is supported by several grants from NIH, NSF and internal MD Anderson funding.
Recent talks/interviews include an overview of Bayesian methods for Integrative Modeling in ‘Omics data and a (very high-level) overview of Big Data in Oncology. A recent radio interview about our imaging-genetics work (given by one my students on our joint work) may be found here and some impact of our work in clinical settings here.
Dr. Baladandayuthanpani's wife is an architect/researcher, and they have two boys. He is also a rabid cricket fan who gets up early to watch matches on TV with his two sons. He has said that if he hadn't gone into math he would have been a sports journalist, or a barista since he loves making different types of coffee.